Multilevel functional principal component analysis

The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS206 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

MODELING SLEEP FRAGMENTATION IN POPULATIONS OF SLEEP HYPNOGRAMS

We introduce methods for the analysis of large populations of sleep architectures (hypnograms) that respect the 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. By applying these methods to the hypnograms of 5598 subjects from the Sleep Heart Health Study we: 1) provide the firrst analysis of sleep hypnogram data of such size and complexity in a community cohort with a 4-level comorbidity; 2) compare 5-state 20-transition-type sleep to 3-state 6-transition-type sleep for a check of feasibility and information-loss; 3) extend current approaches to multivariate survival data analysis to populations of time-to-transition processes; and 4) provide scalable solutions for data analyses required by the case study. This allows us to provide detailed new insights into the association between sleep apnea and sleep architecture. Supporting R as well as SAS code and data are included in the online supplementary materials

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. This paper is a revamping of Modeling multilevel sleep transitional data via Poisson log-linear multilevel models available at: http://www.bepress.com/jhubiostat/paper212

LASAGNA PLOTS: A SAUCY ALTERNATIVE TO SPAGHETTI PLOTS

Longitudinal repeated measures data has often been visualized with spaghetti plots for continuous out- comes. For large datasets, this often leads to over-plotting and consequential obscuring of trends in the data. This is primarily due to overlapping of trajectories. Here, we suggest a framework called lasagna plot ting that constrains the subject-specific trajectories to prevent overlapping and utilizes gradients of color to depict the outcome. Dynamic sorting and visualization is demonstrated as an exploratory data analysis tool. Supplemental material in the form of sample R code additional illustrated examples are available online

Insulin Action, Glucose Homeostasis and Free Fatty Acid Metabolism: Insights From a Novel Model

Glucose and free fatty acids (FFA) are essential nutrients that are both partly regulated by insulin. Impaired insulin secretion and insulin resistance are hallmarks of aberrant glucose disposal, and type 2 diabetes (T2DM). In the current study, a novel model of FFA kinetics is proposed to estimate the role insulin action on FFA lipolysis and oxidation allowing estimation of adipose tissue insulin sensitivity (SIFFA). Twenty-five normal volunteers were recruited for the current study. To participate, volunteers had to be less than 40 years of age and have a body mass index (BMI) < 30 kg/m2, and be free of medical comorbidity. The proposed model of FFA kinetics was used to analyze the data derived from the insulin-modified FSIGT. Mean fractional standard deviations of the parameter estimates were all less than 20%. Standardized residuals of the fit of the model to the FFA temporal data were randomly distributed, with only one estimated point lying outside the 2-standard deviation range, suggesting an acceptable fit of the model to the FFA data. The current study describes a novel one-compartment non-linear model of FFA kinetics during an FSIGT that provides an FFA metabolism insulin sensitivity parameter (SIFFA). Furthermore, the models suggest a new role of glucose as the modulator of FFA disposal. Estimates of SIFFA confirmed previous findings that FFA metabolism is more sensitive to changes in insulin than glucose metabolism. Novel derived indices of insulin sensitivity of FFA (SIFFA) were correlated with minimal model indices. These associations suggest a cooperative rather than competitive interplay between the two primary nutrients (glucose and FFA) and allude to the FFA acting as the buffer, such that glucose homeostasis is maintained

Association between Sleep Duration and 24-Hour Urine Free Cortisol in the MrOS Sleep Study

Context Short sleep duration is associated with adverse health outcomes, but the mechanisms involved are unknown. It has been postulated that short sleep duration may elevate cortisol levels, but studies have had conflicting results. It is unclear whether these differing findings may be due to methodological issues, such as assessment of sleep duration. Specifically, objective versus subjective methods of measuring habitual sleep duration may account for the conflicting results found in epidemiological studies. Objective: Our goal was to determine whether habitual sleep duration, measured objectively (by actigraphy) and subjectively (by self-report), was associated with 24-hour urine free cortisol (UFC), a measure of integrated cortisol secretion. Our secondary goal was to determine whether slow wave sleep (SWS, determined by polysomnography) was associated with 24-hour UFC. Design/Setting Cross sectional study of community dwelling older men. Patients/Participants 325 men (mean age = 76.6 years, SD = 5.5) from the Portland site of the MrOS Sleep Study, who underwent 24-hour urine collection, polysomnography, actigraphy and sleep questionnaire. Primary Outcome 24-hour UFC. Results: In this study of community dwelling older men, self-reported sleep duration was inversely related to 24-hour UFC levels. Participants reporting 5 to 8 hours of habitual sleep. However, sleep duration determined by actigraphy was not associated with 24-hour UFC in either univariable or multivariable regression models. SWS was not associated with 24-hour UFC. Conclusion: Objectively measured (i.e., actigraphic) sleep duration is not associated with 24-hour UFC in these community dwelling older men. This finding, together with prior studies, suggests that elevated levels of integrated cortisol secretion is not the mechanisms by which short sleep duration leads to adverse health outcomes

Where to Next for Optimizing Adherence in Large-scale Trials of CPAP?

Large-scale randomized trials of positive airway pressure (PAP) efficacy have been largely negative yet PAP adherence was notably sub-optimal across the trials. To address this limitation, evidence-based PAP adherence protocols embedded within the larger trial protocol are recommended. The complexity of such protocols will be dependent on adequacy of resources, including funding and inclusion of behavioral scientist experts on the scientific team, and trial-specific considerations (e.g., target population) and methods. Recommendations for optimizing PAP adherence in large-scale trials are set forth that address rigor and reproducibility

Obstructive Sleep Apnea and 15-Year Cognitive Decline: The Atherosclerosis Risk in Communities (ARIC) Study

Prospective data evaluating abnormal sleep quality and quantity with cognitive decline are limited because most studies used subjective data and/or had short follow-up. We hypothesized that, over 15 y of follow-up, participants with objectively measured obstructive sleep apnea (OSA) and other indices of poor sleep quantity and quality would experience greater decline in cognitive functioning than participants with normal sleep patterns

Bedtime habits in adults with and without type 2 diabetes

This study aimed to identify determinants of objectively-estimated bedtime habits and to determine if these bedtime habits differed between adults with and without type 2 diabetes. Adults with accelerometry data from the National Health and Nutrition Examination Survey 2003-2004 and 2005-2006 cohorts were classified as having no diabetes or type 2 diabetes and matched for age, gender, and BMI across the two groups. Multivariate linear regression models assessed bedtime habits (time-in-bed, early versus late bedtime periods, regularity), chronotype (mid-points), and type 2 diabetes status. While the results indicated no differences in bedtime habits between adults with and without type 2 diabetes, an interesting finding was the support for an association between objectively-estimated earlier bedtime midpoints and greater physical activity